{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-04-17T06:02:44.023259Z",
     "start_time": "2025-04-17T06:02:39.897408Z"
    }
   },
   "source": [
    "from sklearn import datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# 加载鸢尾花数据集\n",
    "iris = datasets.load_iris()\n",
    "X = iris.data\n",
    "y = iris.target\n",
    "\n",
    "# 划分训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "\n",
    "# 创建SVM分类器\n",
    "clf = SVC()\n",
    "\n",
    "# 训练模型\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "# 预测\n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "# 计算准确率\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f\"Accuracy: {accuracy}\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 1.0\n"
     ]
    }
   ],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-17T06:02:45.206547Z",
     "start_time": "2025-04-17T06:02:44.056891Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import svm\n",
    "from sklearn.datasets import make_blobs\n",
    "\n",
    "# 1. 生成简单的二维数据（两个类）\n",
    "X, y = make_blobs(n_samples=40, centers=2, random_state=6)\n",
    "\n",
    "# 2. 创建线性 SVM 模型并拟合数据\n",
    "clf = svm.SVC(kernel='linear')  # 使用线性核\n",
    "clf.fit(X, y)\n",
    "\n",
    "# 3. 可视化\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=plt.cm.Paired)\n",
    "\n",
    "# 画出分隔超平面\n",
    "ax = plt.gca()\n",
    "xlim = ax.get_xlim()\n",
    "ylim = ax.get_ylim()\n",
    "\n",
    "# 创建网格以评估模型\n",
    "xx = np.linspace(xlim[0], xlim[1])\n",
    "yy = np.linspace(ylim[0], ylim[1])\n",
    "YY, XX = np.meshgrid(yy, xx)\n",
    "xy = np.vstack([XX.ravel(), YY.ravel()]).T\n",
    "Z = clf.decision_function(xy).reshape(XX.shape)\n",
    "\n",
    "# 画出分隔边界和间隔\n",
    "ax.contour(XX, YY, Z, colors='k',\n",
    "           levels=[-1, 0, 1], linestyles=['--', '-', '--'])\n",
    "\n",
    "# 标出支持向量\n",
    "ax.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1],\n",
    "           s=100, linewidth=1, facecolors='none', edgecolors='k')\n",
    "\n",
    "plt.title(\"SVM Linear Kernel Example\")\n",
    "plt.show()"
   ],
   "id": "b866ddad07bcaec0",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-17T06:02:45.859932Z",
     "start_time": "2025-04-17T06:02:45.782289Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn import svm, datasets\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 加载简单数据\n",
    "X, y = datasets.make_circles(n_samples=100, factor=0.3, noise=0.05)\n",
    "\n",
    "# 使用带高斯核（RBF）的 SVM\n",
    "clf = svm.SVC(kernel='rbf', gamma=1)\n",
    "clf.fit(X, y)\n",
    "\n",
    "# 可视化\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.coolwarm)\n",
    "plt.title(\"SVM with RBF Kernel\")\n",
    "plt.show()"
   ],
   "id": "7d44b366f4d590b2",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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7ZQMkxIbQsBEhZpSRLdeZuACqpCYzu8x8ARGzSE0vxfufaSYugOr1LpMLeO/TK0hNL7VcgITYGJMmL0eOHMGwYcMQEBAAjuPw999/V/mcw4cPo127drCzs0PDhg2xYsUKU4ZIiFn5eMnA69ntneMAX2+Z+QIiZrFlRzIUCkFr4soYoFAI+HtnsvkDI8RGmTR5KSoqQqtWrbBs2TKD2sfFxWHIkCHo3r07YmNj8cEHH2D69OnYtGmTKcMkxGyG9veHUEXPy7ABNGRU1xw7nQVB0P24IADHTmWZLyBCbJxJ57wMHjwYgwcPNrj9ihUrEBwcjG+++QYAEBkZibNnz2LJkiV4+umnTRQlIebTt7s3tuxIwvXbBZXezHgeCG/kjL49fCwTnJnk5pVj654UHDiWgZISJRqFOmLkkAC0b+UGjtPTLWXDysv1ZC4PyQ1oQwhRsaoJuzExMRgwYIDGsYEDB2LVqlUoLy+HRFJ5Rn5ZWRnKyv6bI5Cfn2/yOAmpKYmEx9eftsTSlXew93A6lEpVN4yI59CvpzfemtoEUkndnYp2934hpn9wEfmFCvUQSkpaKQ7HZOLJIf54a2qTOpnANI9wQVpGKZQ6VsGLeA4tIlzMGxQhNsyqkpfU1FT4+vpqHPP19YVCoUBmZib8/St3p8+fPx/z5s0zV4iE1JqDgxgfzYzAa5Ma4urNfDAGRDV1gYe71NKhmZRCIeCdeVdQUKTQmPuhfDiOtmVHCsIbOtfJlVZPPdEAew6l63xcKTA8NbSBGSMixLZZ3Ue8xz91sYd/5XR9Gps9ezby8vLUXwkJCSaPkRBjcHeTolsnL3Tv7FXnExcAOHoqCxmZZTrnfnAcsGFLgvp3vi5pHuGCl8eFAlAND1YQPfz/V8aHIaop9bwQYiir6nnx8/NDamqqxrH09HSIxWJ4enpqfY5MJoNMRqszCLF2sZdzIRZxUCi1JyeMAfFJJcjNL4e7a91L5sY/G4KmjZ3x+9+JuPhIkbrRIwPRqa2HZYMjxMZYVfISHR2NrVu3ahzbs2cP2rdvr3W+CyHEdJJSS5CaVgoXZwkahznWei5KHexQqbZObT0oUSHECEyavBQWFuLOnTvq7+Pi4nDhwgV4eHggODgYs2fPRlJSEtatWwcAmDp1KpYtW4a33noLU6ZMQUxMDFatWoUNGzaYMkxCyCPuxBXimx/v4MKVPPWxwAB7vDohDD27eNf4vK2bu2LLDt21TDgOaOBvDzcX2/igUlqqxN7D6dh9KA15+eUI9LfHsIH+6NzOA7y+Yj6EkFrjmAkHmA8dOoTevXtXOj5hwgSsXbsWEydOxP3793Ho0CH1Y4cPH8bMmTNx9epVBAQE4P3338fUqVMNvmZ+fj5cXV2Rl5cHFxcaQyakOu7eL8Qr78ZCLhe0zk35aGYEBvXxrfyAAcrLBTzz0ink5Mp1znt5Z1oTjBwcUKPzm1Nmdhmmf3AR8Ukl6u0eRLxqf6qe0V6Y914kxGKrm1JIiFWrzvu3SZMXS6DkhZCam/nxRZy7lKszuXBwEGHrumjIZKIanf/W3QK8+eElFJUo1NeoeNMfNsAP770ebhNLpae9H4urN/Kh1PLvxHHAxNEhmPx8qNnjIsSWVef926rmvBBCLCctoxRnLuTqbVNcrMSRk1no37NmhfTCGzlj/Q8d8O/uFOw7mq4uUvfkkAB0budhE4nLrbsFuHRNdz0pxoA/tybhhVHBkEltu/fldlwhTp7NhkIhIKKJMzq28YBIZP2vEan7KHkhhAAA0jOr3hBSJOJqvYGgh7sUE8eEYOKYkFqdx1LOXcoFz0HvNg+FRQrcu1+IyHDb7P3Nyy/HJ4uu4dzFXPC8qlSFUsng5yPD57OiENHE2dIhknrOtj8WEEKMxtWAibKCktnMhFpTYQyAAZ0Ptjoer1QyvDXnEmIv5wJQ7btUUQk6PbMM0z+8iOTUEgtGSAglL4SQh4IbOKBJQ0foG7kRiTn0iPYyX1BWqGUzF72bLAKAvR2PsGBH8wRkZDFns3DzTqHWexQEoKxMiT/+STJ/YIQ8gpIXQgDkF5bjj38S8fGCa5iz+Bq27k5BaamOjWjqsFcnNNT7+Lhngg3qoanLopq6ILyhE0Q6lkPzHDBiUADs7Wo2qdnS9h/N0KgC/DilAOw6mGa+gAjRgpIXUu+dic3GUxNP4n+r7uLwiQwcPJaBhctu4ZnJp3DjToGlwzOrjm098MXsKLi5qhKUil4YqYTDi2ND8OJY25ynYkwcx+Gz2c3g4S7R6KWq+P/Wzd0wZVyYZYIzgvyC8ip7lopLFOYJhhAdaKk0qdcSkosx/vWzUChYpQqwPA84Oojx+8qO9a63QaEQcPJcNpJTS+HsLEa3jl5wdqL5/Y/KLyjHv7tTsOtAGvIKytHAzx4jBvujfw8fm67x8vXK2/h7Z4p6nos2AX52+OOnTmaMitQHtFSaEANt2poMQVk5cQFU4/uFRQps35eKsU8FmT84CxKLeXTrVL/ntlTFxVmCF54JxgvPBFs6FKPq2tETm7bpr4T85BDrLyRI6jbb/XhAiBEcjsnUWmisAmPA0ZOZ5guIEAtKSSvF50tv6FxMxXFAeCMnPGkDVZBJ3UbJC6nXyhVVDO4DKJNX3YaQuuDrlbeRl1+uc5m3vZ0I337eCnY2OhmZ1B2UvJB6rWkjJ70rK0Q8h0gqyEXqgYysMsSczdbbE1lcosT12/VrEjuxTpS8kHrt6aEN9K6sUAoMI2l8n9QD8YnFWud+PYrjgPvxReYJiBA9KHkh9Vp0ew889YQqOXl02WtFb8zUCWFoEuZkgcgIMS9DhoIYg83WryF1C602IvUax3GY+UpjNI9wwR//JuLG7UJwHNAqyhVjnwpCdHtPS4dIiFk0bewMT3cpsnLkOtuIeA5dOtDvBLE8Sl5IvcdxHAb08sWAXr5QKhk4DuB1VE8lpK4SizhMei4ES5bf1vo4xwEjBvvDw11q5sh0i08sxj+7knHrbiHs7ETo0dkL/Xr6UO9QPUDJCyGPEIkoaSH114hB/sjNL8fq9fcBDuA5Doyp5n4N7O2L6S81snSIahu2JOD71fcg4jkoBdWHjpiz2Vi94T6+/aIVghs4WDpEYkJUYZcQQoiGjKwy7DqQhtT0Uri6SDCglw9Cg6xno8njp7Pw/mdXtD4m4gFvLxl+X9nRpisd10dUYZcQQkiNeXvKMG6U9VYOXr8pHjwPrSsFlQKQml6Go6ey0Lurt/mDI2ZBaSkhhBCbUVamxKVr+XpLHIhEHE6fzzZfUMTsKHkhhBBiM/QV0VNjDApFnZoRQR5DyQshhBCbYW/HIyjAXqMu0+MEAYgMp8rYdRklL4QQQmwGx3EYNbyBzmrAHAfY2fEY2NvXvIERs6IJu8QqpGWUYtveVDxIKIadnQi9unihU1sPWrpMCKlkxKAAXLiShwPHMsBzgPAwkRGJOHAc8NmsKDg60NtbXUZLpYnFbdqWhG9/vANwqvLjPM9BqWRo0tARX81taVVFsQgh1kEQGPYeTsembUm4+6AIMgmPnl298ezwBggLtp5l3cRw1Xn/puSFWNSxU5mY9flVrY+JeCC8kTN+/KoNOH0D3IQQUoWSUiUOHMvAg4Sih7273mgYQkmONaE6L8Rm/LxRf72G67cLcOFKHtq0cDNpHJlZZdi2LxVxD4ogk4nQI9oT0e08adiKkDrg8IkMfPHNTRSXKCEWcWCMYfVvD9Ctkyc+eTsSDva0nYCtoeSFWExefjmu3y7Q20Yk4nD8dJZJk5d/d6dgyfJbqm+YakLgjn2paBjigKWftoSXh8xk1yaEmNaFK7n4eME19bwYhfK/wYYTZ7Iwd/E1LPqkhYWiIzVFq42IxZSXV12wgQMgN6BdTZ06n41Fy25BEFS9P8LDfVwA4EFCMd6ZexmCUKdGVgmpV9ZseKD6Q6KFIAAnzmTj5h39H6KI9aHkhViMm5sUri4SvW0USoYmYU4mi+GXP1TDVtooBeBOXBHOXcwx2fUJIaZTUKjAuUu5VVbjPXQiw3xBEaOg5IVYjFjE4akhAeB1fCriOMDBXoR+PX1Mcv3iEiUuXM2r8g/bsdNZJrk+IcS0SkqVVbbhABQVV92OWBdKXohFvfBMEKIiXCpVyxSJOPA8h7nvRsLezjST6QwZtgIAudx0w1aEENNxc5XA3k7/25xSYAgKsDdTRMRYKHkhFiWTifDNZy0xdUIYfL1VE2NFIg49o72wckkbdOngabJrOzuJ4VlFDRmlkqGxCYetCCGmI5XwGDrAX+fQMKDqAaZqvLaHVhsRi5PJRHj+6WA8/3Qw5OUCxA97XUyN5zk8PTQAP/16X2epcZmMyowTYssmjg5BzNlspKSWaGzqWFGZ9+1pTeDirH/uHbE+1PNCrIpUwpslcakwemQQWkW5Vh624gGeBz55KwJOjpTjE2KrXF0kWLm4DYYPCoBM+t9bXnhjZyz6pDmG9ve3YHSkpqjCLqn35OUCNm1LwqZtSUhNLwPPA906eeH5p4MQ1ZR+hgipK0pLlUjPKoODnQhenlS/ydrQ9gCUvJAaKpOrhq2osi4hhJgXbQ9Aaq20VIk9h9Ox/0g6CgoVCA12wIhB/mjZzLVO7zP0aLcyIYQQ60TJC6kkNb0Ub3xwESlppeAe7vR8934R9hxKx8jB/nj71SZ1OoEhhBBi3ehjJtHAGMOsz68gPaPs4feq4xUl8//emYLN25MtFR4hhBBCyQvRdOFKHu7EFamTFW1+25xA+/0QQgixGEpeiIazF3OqnKyallGGlLRSM0VECCHmo1QyxMUX4e79QpRRdW2rRXNeiAZDe1T09cwQQoitEQSGP/9NwoYtCcjMlgMAHB1EGDk4AC+ODaXJ/FaGXg2ioXmEK5RK/YmJq4sEAb52ZoqIEEJMizGGr364jf+tuqtOXADVho2/bU7AO3MvGbwXGjEPSl6Ihs7tPODnI9O5FwjHAU8PDYBYTD86hJC64fL1fPyzK0XrY4wBsZfzsOtAmpmjIvrQOxDRIBJxWPBRczg6iDUSmIqK/Z3aeWDcM8GWCY4QQkzg390peuf6cRywZQetsrQmNOeFVNI4zAm/LGuPzTuSsfdQGopKlAhu4ICRgwPQr6cPxFR9lhBSh8QnFesdLmcMSEotMWNEpCqUvBCtvDxleHlcGF4eF2bpUAghxKRcnCTqgpy6ODrQ26U1oWEjQggh9Vq/Hj56ExeeBwb29jFfQKRKlEoSQgip13p388Yvfz5AYnIJlI8tKuJ5wMlRjKefaGCZ4IwoN68cf+9Mxs4DqcgvUMDf1w4jBwdgUB9fSCW21ZdBu0oTQgip9zKzy/Dhl9dw9WY+eB7gOA5KJUOAnx3mfxiFRqFOlg6xVhKTS/DarAvIyZNDeJigVQyVtYh0wdJPW8LeTmTRGKvz/k3JCyGEEPLQtVv5OBObA6XAENXUBR1au4PnbXuRAmMMk948h7gHRZV6lgBV79JTTzTAjJcbmz+4R1Tn/ZuGjeqwsjIlDhzLwPXbBZCIOXRq64H2deAXkRBCTKVZuAuahdetD75Xb+bjTlyRzscFAdi2JwUvvxAKBxuZmGwbUZJqO385Fx9+eRUFhQqIRRwYgI3/JCEs2AGL57SAnw9VyCWEkPrg8nXVUJigp0hwaZmAe/FFaB7har7AasG2ZugQg8QnFuOdOZdRWKQAACiUTF3DID6xBDM+ugg5lbomhJB6QcRzeldTVbClXnlKXuqg3/9OhFLJtP6wKgWGxJRSHDqeYf7ACCGEmF27Vm5VJi9OjmKbmpRMyUsddOBYut5dn3kOOHwi04wREUIIsZRGoU5o29INIl171gEYNbyBTe2cbTuREoOVlekfEhIYUFSiMFM0hBBCLG3eu5EICXIE8N9edRXJTO/u3pgwOsRCkdUMTditg4IaOCAuvkhnN6GIBxoGO5o3KEIIIRbj7ibF/33dFgePZWD3wTTk5pUjMMAewwb4oX1rd3Cc7cx3ASh5qZOeHhqAxd/f1vm4UgCGDfQ3Y0SEEEIsTSrhMbC3Lwb29rV0KLVGw0Z10BP9/NCprTseT6Qrvn/phVCEUc8LIYQQG0XJSx0kFvNY8FFzvDwuDJ7uUvXxRqGOmPdeJCba2NgmIYQQ8ijaHqCOUyoZcnLlEIt5uLlKLB0OIYQQohVtD0DURCIOXp4yS4dBCCGEGA0NGxFCCCHEplDyQgghhBCbQskLIYQQQmwKJS+EEEIIsSk0YZcQQgixIjm5ciSnlcLJQYzgQHubq35rDmbpeVm+fDnCwsJgZ2eHdu3a4ejRozrbHjp0CBzHVfq6ceOGOUIlhBBCLCI5tQQffHkFI8bH4JV3YvH8tDN44bWzOBxDG+k+zuTJy8aNGzFjxgx8+OGHiI2NRffu3TF48GDEx8frfd7NmzeRkpKi/mrSpImpQyWEEEIsIiWtFC+/HYvjp7IgPFJ9LT6xGB9+eRXb9qRYLjgrZPLkZenSpZg8eTJeeuklREZG4ptvvkFQUBB++OEHvc/z8fGBn5+f+kskEpk6VEIIIcQiVq67h4LCcigFzeMVZWS/XnkHxcUK8wdmpUyavMjlcpw7dw4DBgzQOD5gwACcOHFC73PbtGkDf39/9O3bFwcPHtTZrqysDPn5+RpfhBBCiK0oKFTg4LHMSonLo+RyAfuPZpgvKCtn0gm7mZmZUCqV8PXV3MHS19cXqampWp/j7++PH3/8Ee3atUNZWRl++eUX9O3bF4cOHUKPHj0qtZ8/fz7mzZtnkvhNgTGG2Mu5OHg8E8UlSgQ3sMcT/fyoCi4hhNRTGVllUAr6d+oRiTgkpZaYKSLrZ5bVRo/PlGaM6Zw93bRpUzRt2lT9fXR0NBISErBkyRKtycvs2bPx1ltvqb/Pz89HUFCQkSI3rvzCcrz/6RVcvp4PkYgDYwxgwKrf7uPNKY3x9NAGlg6REEKImbk4Vf1WLDAGF2fan66CSYeNvLy8IBKJKvWypKenV+qN0adz5864ffu21sdkMhlcXFw0vqzVR/Ov4epN1bCWUskgCIDAAEFQjWceoRnlhBBS73h5ytAi0gW8nhXRjAF9unmbLygrZ9LkRSqVol27dti7d6/G8b1796JLly4Gnyc2Nhb+/v7GDs+srt/Kx/lLuRB0jGlyHLB24wPzBkUIIcQqTHkhFIDqveBxHAcMH+gPPx878wZlxUw+bPTWW29h3LhxaN++PaKjo/Hjjz8iPj4eU6dOBaAa9klKSsK6desAAN988w1CQ0MRFRUFuVyOX3/9FZs2bcKmTZtMHapJHT2VBRHP6RzXZAy4dbcQmdll8PKg+S+EEFKftG3pji8+iML8724iv0ABkYiDIDBwHDBiUADenNLI0iFaFZMnL6NHj0ZWVhY+/fRTpKSkoHnz5tixYwdCQkIAACkpKRo1X+RyOd555x0kJSXB3t4eUVFR2L59O4YMGWLqUE2qTC5ozai1tSOEEFL/dO/shU7tPHDsVBYSk4vh6CBGz2gvWtChBccY0z/F2cbk5+fD1dUVeXl5VjX/Zce+VHz57U29bRzsRdi2vgukEtpyihBCSP1Snfdvepc0kz7dvOHoINLZ+8LzqjFNSlwIIYQQ/eid0kzs7ESY804keJ6D6LF/dZ4HGoY4YtJzIZYJjhBCCLEhlLyYUZcOnlixqDW6dvJUL4lzc5VgwugQLF/YBo4OtMk3IYQQUhWa82Ih5eUC5OUCHOxFtN05IYSQeq8679/0Ud9CJBIeEprfQgghxAQys8qwdU8K7sQVQSrl0a2TJ3p09qoz7zuUvBBCCCF1yPZ9qVj0v1tgYGBMVeRu7+F0BPjZ4ZvPWiLAz97SIdZa3UjBCCGEEILzl3Mx/9ubUAqqLWjYwy1oACAtvRQzP74EhcL264lR8kIIIYTUEev/igev451dKQBJqaU4dirLvEGZACUvhBBCSB2gUDKcjs3RuYceAIh44MQZSl4IIYQQYgWUStUcF30YAHm57S8ypuSFEEIIqQNkUh4N/O307qPHGBDeyMl8QZkIJS+EEEJIHTFqWKDOxzgAYhGHIX39zBeQiVDyQgghhNQRI4cEoEsHTwDQ6IER8arvP3orAm6uEgtFZzxU54UQQgipI8QiDl98EIWtu1Pw17YkPEgohkjEoVsnT4x9KghRTa238nx10PYAhBBCSB2lVDLwPGxiGxraHoAQYvWUZXJkHz0DZWExnCIawimikaVDIqTOEYmsP2mpCUpeCCFmxRhD3DdrcGf+CpTn5KmPu3dpixY/fAbnZo0tEpcgl0OekQORoz0kbtRrS4g1owm7hBCzujX3W1x/b6FG4gIAuacu4kSPMSi6fd+s8ZTn5OHauwuwx68z9of2wB7vDjg5YAKyjpw2axyEEMNR8kIIMZuSxFTcWbBS62NMqYSyqBi3Pltmtnjk2bk43n007v9vHZQFRerj2UfO4GT/CUjZtMtssRBCDEfJCyHEbJI3/KsqNqEDUyiR8udOKAqLdDcyotufLkPxnXgwpVIzDqUSYAwXJ88yWyyEEMPRnBdCiNHkX7yB+NV/ovDWPSgLiiCUlQM8B8cmoQiePAoliWngeB5MqXvzFaZQQJ6ZA7GTo0ljVZaUImHtX5USl/8CYVAWlyD5jx0IfnGUSWMhhFQPJS+EkFpjjOH6+4sQ9/VqVTWsx5KTgos3kLJxO5xbNNWdLDzEiXhIPNxqFIeypBQpm3ah4MotiBzs4Tu8H1xbR2ptW5qcDmVRif5YxGIUXr9bo1gIIaZDyYuBysqUuPegCAxAw2BH2NmJLB0SIVbjwYrfVIkLUClxAaBOWAqu3IK+neM4sQi+w/tB4lL9vVfSdx5G7Li3ocgrACcRAwLD7c+WwXtAN7T57RtIXJ012osc7as+KWMQOzlUOxZCiGlR8lKF8nIBa35/gE3bklBUrPoD7GAvwsghAXjp+VBIJTWfNnQ7rhCbtiXh/MVcgAM6tnHHU080QMMQ03aXE2JMTKnE3UU/GtiYgZdJIZTJKz8m4sFLpQj/5I1qx5B75hLOPjVNnSSxcoX6scz9MTj3zGvotOdnjUJddn7ecOvYErlnrwCC9mEsplDCb+SAasdDCDEtmrCrhyAwfLTgGn75M16duABAcYkSGzYn4P3PrkChrFmB4q27U/Di9HPYuT8NyWmlSE4txdbdqZj4xlnsPphmrFsgxOQKb91HaWKqwe2FMjmCJo8Cb2+ncdypaSNEH/gVzlFNNI4zxpCyeTdO9H4eO51bYpdbG5wfOwM5py6q29yZ/4OqR0dLrw5TKpF16BRyjp+r9FiTj1/XmbhAxMN7cE+4tIow+N4IIeZByYsex09n4fjpLK293IwBZ2JzcOh4RrXPe/teIRZ9fwsMqtLNFZQCg8CAL765gQcJxbWInBDzYfLyaj8naNIo9E86jrYbv0OrVQvQ9fif6HFhK1zbNdc8N2O4OuMznB89HTkx5yGUlkFZVIzULXtwovtoJP7yN5SlZUjbflDvXBpOLNK67NlnUE+0/L/54GVSgOPAScTgxKohYe++XdF2/dJq3xshxPRo2EiPf3algOd1fzDjeeCfXcno18OnWuf9a1sSeJ7TSFwexQHYsiMZM16xTKVRQqrDsUkIRI72VU5+rSBysINzVGOInRzh/9RAvW1Tt+zBg+XrVd88MpeGKVSJyqUps1U9I0LVPaCKfO1LnoMmPAXfYX2QtP5fFN2Kg9jZEX5PDYRb+xYG3Q8hxPwoedEjMblEZ+ICqJKaxGTD/mA/6tzFHJ2JC6D6G332Yk61z0uIJYgc7BE8+VnEff+L1sm6mo15BL04yuBl0PeXrdO6eqkCA5D8xw5IPFxRnp2ntQ0AMIHBMTxU5+NSDzeEvTHeoJgIsUVpGaXYfTAN6ZlyuLtJMKCXD4ICbHcyOiUveri5SpCUWqJzcQQHwNVFUu3zGrK7pw1sAEqIWvi8N5F94jzyzl3RvpqI4wAwuHdshaafv2XweXPPXtGfECkF5J65hOCXn8PdxT/qbMvxHAInPGXwdQ3FGEPe2csovH4XIkd7ePXvVqOVUoSYCmMMq9bfx89/xIPjAJ7jwBjDmg0PMGKQP2ZObQKxDW7eSMmLHgN7++LqjXzdDThgcB+/ap+3Q2t3bN+bCqWOrm6eV7UhxNIYY8g+dhZpWw9AKCmFS6tIBIx5olLPidjJEdEHfsWDlb/jwYr1KL6fCF4iAS+VAIzBoVEwQl5+Dg3GPwmRTGrw9XmJGIK+zk2OAy+VoNE7LyFt634U3binOfeF5wCBodmSD2Dn513Nu9cvL/YaLr74vmr5d8Xl7GRoOHMSwudMByeicgrE8v78NwlrN8YDUH2uEPDf+86/u1LgYC/Cay/a3o7uHGN6ii7YoPz8fLi6uiIvLw8uLrXbGba4RIlJ088iNb200gc6EQ94ecrw8//aw8mxejng3fuFmDT9nM5hehHP4dcf2tt0lx6xTSWJqYj/v43IiYkFUypRfC8BpQkp4MRigFNVvxU5OqDNL1/Bd2gfk8cT+8JbSNm0Sz3HpRKOQ9Q3HyN02vMoz83HrXnfIWHNX+r5N84tI9Dko9fgM6gHcmJiIZTJ4dw8HPZB/rWKq/DGXRyLfgbKktLKvT0cEDJ1LJp/N6dW1yCktsrLBYyYEIP8AoXONhIJh3/WRcPFqfqjCMZWnfdvSl6qkJFVho8XXMOVG/ngOQCcaq5LRBMnfD4rCn4+dlWeQ5udB1Ix/5ub4DhO3QMj4gGAwyfvRKBv9+pNAiaktpL/3IEL498FY4L+oRqOAyfi0eXoRpNPas09cwnHu43WPmtexEPi6oLet/ZpFKBTlpSiNDEVvL0d7AJ8cGf+D7i3dDUU+YXq+H2G9ETzZfNgH1j9nlMAOP/8W0jdrCepAtDrxl44Ngqu0fkJMYbzl3Mx/YOLVbab804k+ve0/HtOdd6/adioCt6eMqxY3AY37hQg9nIuGANaN3dFZBNng+au6DK4jx8imzhj8/ZknL2YA47j0LGNO54cEoDgBtTjQswr/+INxI57u+oJt4B6TsvdxT+h3cbvTBqXW4eWaL1mIS5OngXGmCq+h793ElcXdNqxqnLlXHs7ODYJBQBcfm0O4n/6HXj0IxpjyNh9FCd6jEG3k5sg8/GsVkzK4pIqExdOJELSb/8i/OPXq3VuQoyptFT/VhwVSgxsZ00oeTFQRGNnRDR2rrphNYQGOeKtqU2qbkiIicX9T1V91tBuWKZQIu2ffWBKpcnndjQYOxwe3TsgftUfyD11EbxUAu+BPdDghRF6J8fmX7qB+B9/1/oYUyhRlpyOe1+vRuT8dwEA5XkFSFy7CUm//Qt5Th6cwhsi+OXR8B3aBxz/X0ms8px8vYkLAIDnUJZa/RpQhBhTSKBhH4RDg2zvAzMlL4QQpG8/WPUb8mOYUglBXg6RveknptoH+aPp3Dc1jgkKBRQFhRA5OWrtBU38eTM4sUjnfTGlEgmr/kTEl++g5H4iYvqOU1UKftizVBqfgozdR+A7sj/a/vY1eIlqToDEwxWcRKyxBUGlcwsC7Br41vR2CTGKBv72aNfSDbFXcrWOvPI8EBRgjxaRtZ9iYW5UYZcQAqGaiQsA2DXwA28nM0E0+hVcuYXYF97CLpdW2O3RDnv9OuHGR0tRnqu5MrAkKRVMX6EmAOU5eVCWyXH2mddRlpKuscy7YtVS2j/7cHfhSvVxkb0dAsYMVVfi1UpgaPD8iBrcHVCanIabc77BoaiB2B/aA6dHvIL0nYdRx6YnEjN557UmcHIUP5xT+R8RD0glPD6aGVGrKRCWQskLIQTuHVtVb/iH4xAy7Xmz/9HLOnoGx6KfVq1AetjzUZ6dh7tLfsLxrs9CnvVfcUeZt6fGcI82Ikd75J25hIJLN3T3PDGGuP+tgyD/bzPJ8I9fV/X46Pg3a/TOS3AIaVDNu1NNUD7cYgjuLFyJolv3UZqUhozdR3Fm+Mu4/OonlMCQagsKcMCqr9thUB9fSMSq31eeB3p28caPX7VFZLjt9boAtNqIEAIgfedhnBn+suFP4Dj0vX8YdgHmGxoRFAocCOuJsvRsrauPOJEIgROeQsuVnwMAck5ewInuo3WejxOJEPzyGMj8vXF73v/07o0EAN3P/wuXFk3V3xfeuItLr36CnGNn1cfErs5oPHsqGr41udqJnbK0DAca9oI8O1fnxOkWP3yG4JeerdZ5CalQVqZEXoECTo5iOJhhuLe6qvP+TT0vhBB4D+qBhu+8pPqmit4KAABjSNt20LRBPSZ9xyGUpWbq3GyMKZVIWv8Pyh8uiXbr1Aq+I/urCtU9hhOJIHZxQsO3J2uvCGwAp4hG6HJwPXpe3oF2fy5Dx52r0S/xOBq9/VKNeqRS/toJeUa27hVfHId7X6+m3hdSYzKZCD5eMqtMXKqLkhdCCDiOQ+T8d9F+yw9wbRdVdXuJGKVJqWaI7D8Fl26oiuXpIZTJUXxXVU2U4zi0+XUpgic/W2l+inPzcEQf+g0OIQ3g0b1Dlb0uEg9XODUN0/qYU0Qj+I3sD+9+XSGqxRygnOPn9d8fYyi6FQdFrp6q34TUE7TaiBCi5ju0D5ybh+Ngk7562zGlEjIfLzNFpcLb26kK6FVBZP9fAiGSSdFi+acInzMdGfuOQyiVw6VVhEZxPY9u7eHcPByFN+5qn/fCcQh9bRx4qeHbGtSIoZ01Nji5khBjo54XQsxMUCissuufKZW4Pf8HHOtU9QaGHC+C/6jBZojqP75D+1RZRM+hYTAcmzasdFzm64XA50cgePKoSlWBOY5D+03LIfPz1kwMHk7G9R3eF41nT639DVTBs2cnMIXu5dfgOThFNYHErWZz+fIv3UDqv/uQfexslT1NhFg76nkhxAzK8wsR9+1axK/cgLK0TIgc7NFg7HA0fHsyHBuHWDo8MMZw8aUPkLT+bxhSqa7R+y9XuzJtbTk1bQjfEf2Rtm2/ziSm8Qev1mi+iUPDIPSI3YqEtZuR9Ns/KM/Jg2OTMARPGQ2/Ef2qXLVkDH5P9ocswAdlaVmAtuRCYGj09kvVPm/OqYu48voc5F+4rj4mC/BBxBdvI/CFkbWImBDLodVGhJiYPDsXMb3GovBmnMZkU04sAm8nQ+e960y+R1BVso+dRUzv56tsJ3K0R+NZU9Ho/VcsUhtCUVCIc6PeQOb+E+DEYjAmgAMHJggInzsdTT6YZvaYjCn/0g2cHDAB5Tl5qNi5taLQXtiMSYhc9H61/t1zz15GTK+xEMoVWic6t1j+KYKn6F6RRYg50caMlLwQK3Lx5Q+RtG6L9q56EQ/7oAD0vrnXLJ/udbkw6T0k/75Nb5VduyB/9Ly0HWInRzNGVhljDDknziN543aU5+bDsVEwAic8BYfQQIvGZSzyrBwkrPkLKX/tgqKoGC4tIxH66lh4dGtf7XOd6P08cmLO6+ypEjk5oF/icYgdba88PKl7KHmh5IVYifLcfOxt0AVMXq63Xccdq+Ddv5uZoqrseI8xyI2J1dtG5OiAQbn62xDrUXw/scqJ1wDQ+ufFaDB2uBkiIkQ/2lXagrJy5CgvF+DlIYVYTPOh67vCm3FVJi6cSIT8C9dqlbwwxpBz/BwS121BaXIaZH7eCBw3Eh49Oho0zCD1dFfVd9FTTl/i4Vrj+IhpKEtKoSgshsTdBfxjy6xLk9KqfD4nFpl9yTshxkDJi5EcOp6Bn/94gNv3igAAzk5ijBzsjwnPhsDOzvYLApGa4WVVL69ljNVqGa4glyN2/DtI3bRbPT+CE4uQ+PNm+Aztjba/fwdRFXE0GDsM6dsO6G4g4hE4bmSNYyTGlXf+Km5/uRxpWw8AggCxsyOCXhyFxrNegdTLAwAMmlDNlEpIzbzknRBjoK4BI/j97wR8tOAa7sQVqY8VFCqwflMCpn90EaWltCyxvnJpEQ6Zv4/+RoIAnyG9anyNGx8uRermPQCgnrNS8d/0HYdw7Z35VZ7Db2R/uLSO1LpXDycWQerhhpBXq57QS/4jyOVI2bwbt79cjrhv16L4fqJRzpt5IAbHu49G+raD6p4yRUER7i/7Bce7Pouy9CwAgGOTULi2b6G3YjIvk8FvZH+jxEWIOVHyUkspaaX4fvU9AJWrjAsCcP1WATZtT7JAZMQacCIRGr//iu4GIh6+I/rDsUlojc5fnleABz+s113iXmBIWPWHxoaF2vASCTrtWgPPPp0fHuBRsQ2tY3hDRB/4FXZ+3jWKsT7K2HMU+0N74vzo6bj92TJce28hDob3w4UX34eytKzG5xXKyxE77m0whaLSBHCmVKLkQTJufLBEfSxy4Xuq4nc6hg7D57wBiatzjeMhxFIoeaml7XtT9Ba8ZAzYvD3ZfAERqxMy7XnVHjpQ9WKA49Tl6j27dUDrNQtqfO7so2cglMn1tmHlCmQdPFnluaSe7ui0YzV6XNyOZoveR8SX7yD64Hr0uLAVThGNahxjfZNz8gLOjJgKeaYqYWQKpeqTDGNIWv8vLk6eVeNzp207CHl6lnoZ9eOYUomkDVtR/nALAc8eHdFx60+wC/TTaCd2dkTk4tloWIO6MYRYA5rzUksPEkuq3NctLaMM5eUCJBLKFesjjuMQueA9BI5/CglrN6E4LgFSd1cEPDcUnr0616peilDFZODqtgMA52aN4dyscU1Dqvduf75M9alF2x8GQUDKHztQ8ME0OEc1qfa5C6/eUtW30VOJl8nLURyXCNc2zQAA3v27oc+dA8g6fArFcYmQerjBe2B3iOztqn19QqwFJS+1ZG8vAs9zUCp1ZzBiMQeRiPYjqe+cmzVGs0XvG/Wcrm2aqYYEqsigXdtWvdkiqb3yvAJk7Dmm9/XgxCIk/7EdTefNqPb5RY4OYHpWhKnbOWgmJhzPw6t3NNC72pckxCpRV0At9e7qpTdxEYmA3l29wfOUvBDjcwgLgveA7lon2gKqN0qPHh1p2MdMFHkFVSaS4DiU5xbU6Py+w/rqXc4OjoND4xA4hmvfAZuQmlIqGZJSS5CUWqL3Pc9cqOelljq28UB4IyfcjSuC8rFxaI4DOHAY+3SQhaIj9UHLlZ/jeI8xqnodj1ZSFfGQ+nii1eqaz6kh1SP18QRvJ4OgZ1IuUyrhEFazvwmOjUPg/8xgpGzZrb1qLmMI//h1i2zdQOompZJh4z+J+OOfRGRmq+bXeXtK8eyIQDw7PNBiowrU81JLIhGHr+a1QGS4s/p78cMX09FBhIWfNEeTMCdLhkjqOLsGvuh+ejOazH5VtTMyr0paGr37Mrqf+RsOIQ0sHWK9IbKTIXD8kzp7wgDVjtwNnq95RdtWq+bDZ3BP1bnEItXkbxEP8DwiFr5H1XKJ0TDG8NnXN7B8zT114gIAGVlyfL/6Hj7/+gYsVaSftgcwEsYYrtzIx/HTWZDLBTRp6IQ+3bwhk1GBOmKY8tx8KItLIPX2AC+RWDocUkOlqRk4Hv0MylIzNPeKejg3qdnXHyHs9XG1vk7umUtI/nMnFLn5cGgUjMBxI2EX4Fvr8xJS4djpTMz67KreNos+aY4uHYyzwzxtD2ABHMehRaQrWkRSCXVSPZkHYnD7y+XIPnwaACB2c0HIlNFoNGsqJC7Ua2dr7Py80fX4H7g+azFS/typXhnk2DgE4XPfRMCzQ4xyHbcOLeHWoaVRzkWINn/vTNG7a4iIB/7emWy05KU6qOeFEAtK+u1fXJj4HsBzlearOEc1QfTB3yiBsWHy7FwU30uA2MkBjk0b0lwUYlNGTT6FlPRSvW0C/Ozwx0+djHK96rx/05wXQiykPCcPl175SLU65fHJl0oBBVdv4+7ClZYJjhiF1MMNbu1bwCmiESUuxOY4OlY97cHJ0TIDOJS8EGIhiev/1V8dVyngwY+/Q9BTkIwQQkylXw8fvRXkOU7VxhIoeSHEQgqv31FvE6CLIjcf8oxsM0VECCH/GT7QH+6ukoptzjSIeMDdTYKh/f0qP2gGlLwQYiEiRwfAgBlnIgd70wdDCCGPcXGWYNn81vD3VVVsFon+qxbv72ePZfNbw8XZMisjabURIRbiN7I/4r5erbuBiIdn9w606y8hxGKCAx3w24qOOB2bg4tXcwEArZu7oUNrd4tWjqfkhRALcY9uA48eHZBz/DyYUlm5gcDQ+INXzR8YsXnKMjmKbtwDADhGNIRIJrVwRMSW8TyHzu080Lmdh6VDUaPkhRAL4TgO7f/6HmdHvY7sw6fBicUABzCFErxMipYrP1dtpqdDaUo6Uv7cCXlWDuyDAuA/ajD10tRzQnk57nz5A+KW/QJFbj4AQOLuitDXXkDjD16l4oekzqA6L4RYGGMMuacuInXLbigKi+HcrAkaPD8cEjftP79MEHDjw69w7+vVAGPgRCJ1whO56H2Evvq8me+AWAMmCDg36nWkbT1QeXNIjoPvsD5o9+cycDxNdSTWyerqvCxfvhxhYWGws7NDu3btcPToUb3tDx8+jHbt2sHOzg4NGzbEihUrzBEmIRbBcRzcO7dG5ML30eL7eQh97QWdiQsA3Jr3He4t+T9VbRiBgZUrAMYglJbh6vRPkbj+HzNGT0xBWVKKhJ8349SQF3Gs89OInfAOso6e0buPTNq/+5H2737tu1ozhrR/9yP1n30mjJoQ8zF58rJx40bMmDEDH374IWJjY9G9e3cMHjwY8fHxWtvHxcVhyJAh6N69O2JjY/HBBx9g+vTp2LRpk6lDJcTqlefk4d5Xq/S2ufXJN2C66nkTq1ealIaj7Ubg0kuzkbn/BPLOXUHKHztwss8LuDz1I52v7YMff4fWNa0VRDzif9pooqgJMS+TJy9Lly7F5MmT8dJLLyEyMhLffPMNgoKC8MMPP2htv2LFCgQHB+Obb75BZGQkXnrpJbz44otYsmSJqUMlxOqlbT+ov7AdgJL4ZOSdu2KmiIgxMcZw9pnXUByXoDogqHpRKjZ4TFj9F+K+/Vnrc4tu369cqflRSgFFd+4bMVpCLMekyYtcLse5c+cwYMAAjeMDBgzAiRMntD4nJiamUvuBAwfi7NmzKC8vr9S+rKwM+fn5Gl+E2Dp5Vg7Sdx1G+u4jkGfnqo+X5xZAb8nLhxT5hSaMjphK7qmLyDt7WXM36sfc+3qV1tVpUo+qN4WVulfdhimVyDx0Eonr/0HGvuNU4ZlYJZOuNsrMzIRSqYSvr+Y27b6+vkhNTdX6nNTUVK3tFQoFMjMz4e/vr/HY/PnzMW/ePOMGToiFKAqLcO2d+Uhct0U1lwUAJ5UgcPyTaLZkNhwbB2uf0/AYh7AgU4dKTCBz/3FwYpHe5KUsJQOFt+7DObKRxvEGY4cjL/aa7p8PjkOD50fovX7a1v24Mv1TlCb+9/dZ6uuJZotno8Fzwwy/EUJMzCwTdh/fkIwxpneTMm3ttR0HgNmzZyMvL0/9lZCQYISICTE/QS7H6SGTkbB2kzpxAQAmL0fCmr9w+omX4NmrE+wa+OrufRGJ4NmrExwaUvJii5i+YZ9Hael5CZz4NOwC/bRuOcGJRbAL8kfghKd0njJt+0Gcffo1lCalaRyXp2Xhwvh3aCI4sSomTV68vLwgEokq9bKkp6dX6l2p4Ofnp7W9WCyGp6dnpfYymQwuLi4aX4TYouQ/diAnJlb7vAWlgJzj55C6ZS9a/vQlOBEPPLbklROJIHa0R9S3n5gpYmJs7tFt9Pa6AIDYzQUOTUIrHZe4OiP6wK9wjgoHoPp54ESqRMY5KhzR+3/RWQeIMYZrb31R8Y3WNtffXQBBy9A9qV9S00tx8HgGDsdkIjfPcj8PJh02kkqlaNeuHfbu3Ysnn3xSfXzv3r0YMUJ792V0dDS2bt2qcWzPnj1o3749JFRgidRhCav/VCUkulYK8TziV/2B6H2/oPPedbj5ydfIPnpW9RjHweeJXoj44m04RTTS/nxi9bz6doFDo2CU3E/SXnWZ5xEydazOirkOoYHodmYLck6cR/Yx1c+GR7f2cO/SVm9vd+6piyi+p7/XWp6Rjcz9J+AzqKfhN0RshlLJcD+hCOXlDIEB9nBy1EwPcnLlWLTsFo6dzlLnt2IRh8F9ffHmlMaws9O/yayxmbzC7ltvvYVx48ahffv2iI6Oxo8//oj4+HhMnToVgGrYJykpCevWrQMATJ06FcuWLcNbb72FKVOmICYmBqtWrcKGDRtMHSohFlUSn6I7cQEAQUBJfDIA1RtS9IH1KE1KgzwzB7IAH8i8rad0N6kZjufR/q/vEdN3HMpz8//7eeA5QGDw7NUJTT56Tf85OA4eXdvBo2s7g69blpZpWLuUDIPPSWwDYwz/7ErBuj/ikZ5ZBgCQSDgM6u2LVyc1hIuTBMXFCrw26wKSUko0OuYUSobt+1KRklaKr+a1VG/aaA4mT15Gjx6NrKwsfPrpp0hJSUHz5s2xY8cOhISEAABSUlI0ar6EhYVhx44dmDlzJr7//nsEBATgu+++w9NPP23qUAmxKJmvlyo50TXhkucg8/XWOGTXwFc1B4bUGYqiEjg0CkbemUvqYyJ7O4RMewFN570JXiIBYwyFV2+jNDkNUh8vuLSK0NuzUhWZn3fVjQDIAnxqfA1inX78JQ6//KnZ61ZerkpKLt/Ix8rFbfDv7hQkJJdo/dMkCMDZi7k4eS4bXTtWntphKrQ9ACFW4sGPv+PK63MAPb+RLVZ8juDJo8wXFDGrnJhYxPQbp5r38mgvHM+D4zl02PoTeKkEV2d+gYJLN9QPO0U0QuSSWfAZ2KNG12WM4VDkANXQkY63BKmPJ/o+OAJeTFvi1RXxicUY++oZnY/zPDBpTAj2HklHfGKJ3nbdOnnhyw+iahWP1W0PQAjRpCwtQ86pi8g+cR6KAlVNlsAXRsApsrF6kuWjOJEITlFN0GAsLVetqxhjuPzanMqJCwAIAphSwIWJ7+HUwIkouHJL4+HCm/dwZtjLSNt2oEbX5jgOUd98VPGN1jZRSz+kxKWO2bY3RW9RZkEAtuxMRla2/sKYggD1kJO5UPJCiBkJCgVuzv0W+wK74kS3ZxHT8znsbdAVV2d8DgCI3v8LvAZ003wSx8F7YHdE71sHkb2dBaIm5pB/8QYKLt/UPe+JMcjTMlXLqR9v87C35Mob87RP9DWAz6Ce6PDPSjiEBmoct2vgh7YbvkHA6CdqdF5ivZJTSyuKOOuUnVMODzftE8Qr8Dzg4ykzYmRVozSaEDNhjOHCuHeQsmmXRte8UFKK+yvWIy/2Kjrt+Rkd//0RRXceIPv4OQCqybmOjYItFTYxk5L7iYY11DXSzxhKE1ORdfg0vPpE1ygGn8E94T2oB3JiYlGalAqZrxc8urbT2htIbJ+zkxg8z0Gp1J3ByKQ8hg7ww4qf43T+6AkCMKSfeefeUfJiAjm5cpTJBXh5SCEWU+cWUcncdxwpf+3U/qBSQM6J80ha/w+CXxwFx8YhcGwcYt4AiUVJDCjvb4iKFWk1xXEcPLq0NUosxLr17eGDrXu0V7sHVPt89u/lg5GDA7B1TypSUkuhfKyrhueBVlGuiG5vvsm6AA0bGdXhmEy8OOMcho2LwTOTT2HYuBisXBeH0tKadeOSuiX+//7QWv1UjecQ/+Pv5guIWBX3Lm0NXvWjj5SWzBMDtWvphtbNXR+vdwlAlZRIpDzGPhUERwcxli9ojY5t3TXaiHjVkupFn7Qw6zJpgFYbGc0f/ybiu5/uguM0e3V5Hoho7Izvvmhl9iI+xLocbT8S+Rev620j8XDDgLRTZomHCQKyj55BSXwKpF7u8OobDV6qf2ybmFbiui24OHmWzsfFbi5Q5OrefFbi4Yq+8cd0FrEj5HFFxQp8tvQGjp3KAsdBPYzk4yXDZ7OaIaqp5vtocmoJrt0qgEjEoVUzV3i4G+9nrTrv3zRsZARpGaVY9n93AVQejhYE4PrtAvy5NQnjRtG8hfpM6u2uv4IuAKmnm1liSd99BFdem4uSB0nqYxIPV0R88Q6CX3rWLDGQygLHPwllSSmuz1oEZWGx+ueFs5Oh6bw3YdfADxdeeEvn8yM+f5sSF1Itjg5iLPioOeITi3HibBbkcgFNGjqhYxsPrb0pAX72CPCzt0CkmmjYyAi27U0F9PSYMQZs3p6kuwGpFxqMHaG/gi7PI3D8k7ofN5LMAzE4M/yVSnMjyrPzcPnVj/FgxW8mj4HoFvLKc4j67hNV8cGHPy+8iEdZUhp8BvVAqzUL/5sf83BZs9jFCc2XzUXwlNG1ujZjDIJCUXVDUucEBzpgzMggjH82BNHtPc0+DFRdNGxkBHMWXcOBYxk6Z2JX2L+pO2RSyhfrK2VpGY5HP43CG/cqbb7HiUWQ+niix/l/IfV013GG2mOM4Wjb4Si4dhu61kiKnR3RL/E4RA6W/3RlC5SlZUjZtAt5Zy6DE4vgPagHvPpEg9M2kcAA975ejevvLcTjY9CqWj+N0eXQb+BlUqTvOoLSxDTI/LzgM7hnrZbR5124jntLfkLKlj1g8nLYhwYidNrzCHn1eYjszLsEltRf1Xn/puTFCBZ8dxM7D6Rq26VeTSTicHBzd/C8dWezxLTKMrIR+8JbyDoQo9qvBhwgCHBpE4V2v38Lh4ZBJr1+wZVbONKm6kJ3bX77GgGjhpg0lrog6+gZnHvmdZRn54KTqEbhWbkCTs2aoOPWH2EfHFCt85UkpOBA4z66e+hEPJp8MA3hn7xR29DV0ncdxtmnpgGMaSbVPAf3Lu3QaedqSmCIWVCFXTPr2cVLf+LCc+gZ7UWJC4HM2wOdd69Fj9itaLZ4NiIXvIcux/5At1ObNBIXxhjyLlxH6t97kXXktNG68g3agI/nUZZq2EZ99VnhrTicHjJZtYEiVEkLK1e9TkW37uHkgAlQllav6mjC2k16h6ChFPBg5QYY6zOnoqgY58fOBFMoK/UGQmDIOXEedxf/ZJRrEWJMNGHXCDq19UDTxk64c68Qysc+MHGc6uuFZ0z7iZrYFufm4XBuHq71sZyTF3D5tTkae9fI/LzR9PO3EDThqVpdV+ZvwMZ6gkCbPRog7rufwRQKrb0kTKFE8d14pGzahcDnRxh8zqJbcVW2kadnQVlUDLGTY7Xi1Sb59+1QFhTpbiAIePDDejSePZW2BiBWhXpejIDnOSyZ2wKR4apuLpGIU+8XIeI5tGvlhrSMMr1VDAkBgNwzl3Cy37hKe9eUpWbg0kuz8WDlhlqd37lZY7i0bgathR0eErs6w2dIr1pdpz5I+XNH5d6KR/E8UjfvrtY5xS5O4Dj9f5Y5sQi8kYZx8i9eVw936SLPyIY8Pcso1yPEWCh5MRJ3Vyl+WNQayxe2RreOnupVAIwxnL2Yi9lfXMUL084gJa3UwpESa3b9/UWqISIdcx6uv78QikI9n5QN0GzpB+B47uGcGy2PL55FcxwMoCzSvcsuAEAQoNDXq6GF/6jBqt4cHTixCH5PDjRaLwgvk+rdxVyjHSFWhJIXI+I4DnYyHsdOZ0F4uJJDKUDd45KcWoI3P7qI8nI9y2VJvVX8IAnZR8+g0tjjI5RFJUj9Z1+truPZvQM67VoDxyZhGsdlft5otXYRgiY9U6vz1xdOEY10JoCAKtFwbtakWuf07NkJ7rr2EuI5gOfR6N0p1Q1VJ58neulNlsDzcO3QwqQr4AipCUpejGzDlkTVrH0tn2aUgmoXz0MnaDIkqaw0Ka3KNpxYZFC7qnj27ISel3eg64k/0fb3b9F53zr0vX+4WvMz6ruQV5/XudwcUM17qW7BP47j0OHvFfDs3Un1vUikHtaRuLqgwz8r4dqmWc2Dfoxnz05wbddc97YVgoDG70812vUIMRaagWVkR2Iy9X1wBs8DR09mon9PAyZOknpF5utVZRumVELmY5wN0DiOg1uHlnDr0NIo56tvAsePROqWPcjYe1QzieE5QGBo8skbOidl6yNxc0GnnWuQF3sNadsOQCgtg3OLpvB7coDRq+dyHIf2f6/A6cEvouDKLXBiEZhSUN9Ds8Wz4Dein1GvSYgxUPJiRIwxyKsYEhIEoExOGzUSTUwQUByXAImnG8qzcnW242VS+D05wHyBEZ14iQTtN3+Pe1+vwf1lv6AsNQMA4NysCRq99zIaPFd1PR19XNs0M2oviy52ft7ofvZvpO88jJRNu6EsKoZTZCMEvTgKDiENTH59QmqCkhcj4jgOoUEOuJ9QrLPaLs8DDUNqv8SR1B3KklKce+Z1ZOw5qncVEACEz5kOiauzmSIjVeGlUjR+/xU0eucllKVlgpNIIPVyB8fZVk0nTiSC79A+8B3ax9KhEGIQSl6M7OmhDbBk+W2djzMGDB9YvaqbpG67OuNzZOw7rvpGxyojsYsTwj95A6HTJ5g8npKEFCT/vhVlqZmQBfigwdjhsDOkPkw9xolEsAug2jiEmAslL0Y2dIA/TpzJQszZbI3el4rNhGe83Bj+vjXfg4TULWXpWUhct1nvho2cRIyeV3fBzs/bpLEwQcCND5bg3tLVAM+B43kwpYAbH36FJrNfRZNP3rC5HgVrVpqagaJbcRA52MO1TTPtK4wIIVpR8mJkYhGHLz+IwqZtyfhzayJS01XlwVtGuuKFUcHo3M7DwhESa5J16JT+QmdQlZ3PO3cFdk/0Nmksd75cjntfrVJ9o2SqiZsP3f78e4hdnNFw5iSTxmCtmFKJjL3HkH/xBnipFD5P9IJTeFjVT9SiJDEV1976QrXk/WHSKgvwRZMPpyF4ymhKEAkxACUvJiAW8xg9MhDPjmiAomIlJGIOMhl9qiKVCXK5Qe0q9swxFUVhEe4sWKm3zZ35PyBk2vN6V7yUJqehPDsPdg18IXF3NXaYFpFz6iLOP/cmShNSABEPCAzX31sA3+H90HrtQoidnQw+V2lKOo53HaWqWPtIb1tZchquvDYH8owsNPnwNVPcBiF1CtV5MSGO4+DkKKbEhejk1r5F1Y04Dq5to0wax8UXZ0Eo059IlefkqYroaZF15DRO9BqL/SE9cKTNMOzx74zzY2eg+F6CKcI1m8JbcTg1YAJKk1JVB5QCKsaD07buw+knXqrWJol35q+APC1LZ2/brXn/Q0liaq3jJqSuo+SFEAtyimgEz16ddBYJ40Qi+DzRC/bBppvknbHnKFK37DGorbZy92nbD+LkgAnIiYn976BSQOrmPTgW/QyK7jwwVqhmd3fxT1CWlWkvRseAnJhYJG/cbtC5BLkciT9vBtO3BT3PIfGXLTWMlpD6g5IXQiys1aoFkPl5V14mzfOwDwlAix8+M+n1477/FeqdRKvw+DwPQS7HpZdmq97cH5t0zJRKKPIKcO3d+UaL1ZwYY0j+fZve7RoA1X5ThijPyYeyWP9+SBzHo+RBssExElJfUfJCiIXZBweg+5ktaPLhNNg18AUnlcA+OADhc95At5ObTL7KKPdkbJVv0ADg1qkVnKM09+pJ33EY8swc6CpsxJRKpG8/hNLk2m9pYG6CvBxCaVmV7cqS01F0+36V7cQuTuCqTBIZpJ5uBsVHiCGUSoZT57Px57+J2L4vFbl55ZYOyShowi4hVkDq5YHwT95A+CdvmP3anCE7FPO81h6gojv3wYlE+odCGENxXKLN1UHhpRKIXJygzC+ssm3+5ZtwbBKqt43I3g5+Tw5A6t97dc55YQolAsYMrUm4hFRy7mIOvvjmJtIzy8Bxqs8YYhGHZ4Y1wNSJDSEW2e7KNup5IaSe8xnSS/fGfA+FTZ8AlxZNKx0XuziD6alR8187w1fkWAuO4+Ddv5tBbXk7mUHtGn8wTfVvra2SMs/Df/QTWv+dCamua7fy8fbcy8jIUvUeVnSOKpQMG/9OxHc/3rFgdLVHyQsh9VzYG+NVK2a01RfheYicHdHovZe1Ptd3eF/9QyEcB8cmoVVuUMgYQ/bxc7gw6T0ci34Gp4a8iIS1m6AsKTXoHkqT0pC+6zAyD8YY/BxDRC58D6jiw6nIwQ6ePToYdD6XFk3Radda2Af6qQ7wvOrfXcQjaOLTaLVqQS0jJkTl/369D0FgWkd0GYDNO5KRkma83xVzo2EjQuo5l5YRaPvrUsSOf0fVi1Ix/4XjIHZyQId/f4TMW3txRTs/b4RMewH3/7dO+7wXxtD00xl6C68xxnDljXmIX7lBtauxQgnwPDL3HsedhSsRve8X2DXQPuRUmpqBK2/MQ9q/+9UThsXOjgibMQlNPpxW66q1DiENEPzyGMSv/F17A45D6PSJEDsZvl+ZR9d26H17PzL3n0DB1TsQO9rD54leNjesRqxXXn45Tsfm6G3D88C+I+kYNyrYTFEZFyUvhBD4PzMYbp3bIGHVH8g+fg6cSATvAd0QOOEpSD3c9D43YsG7yL90A9mHTmkc5yRiRH3zEbz6dYVQXg5eItH6/Pvf/4r4lRsA4L+5IA8TkZL7iTj79DR0jfmrUgIkz87Fie5jUJqYorHSSVFQhNufL0NJYgpa/fhldf4ZtIr6+iMo8ouQvGGralUWY6qtExRKBE54Ek3nTq/2OTmeh3f/bgYPSxFSHfmFVU/K5XkOufm2O3mXY9WpsGQD8vPz4erqiry8PLi4uFg6HEIsgjGG/NhrKEvLhMzPGy6tI01Sdp4xhsvT5iDh/zZqbyAWAQolOKkEAWOGovH7r2gst2ZKJQ406vNfETgdog/9Bo+u7TSO3ZzzDe4sXKl3pZRD4xCETZ+AoEnPQGTgvBRd8s5dQeKvf6MsLRN2/j4IHP8UXFpF1OqchJhCUbECQ8aegFKp++2d44DpLzXCqOGBZoxMv+q8f1PyQkgdk777CK69PR9FN++pjzlFNkbU1x/Cq28Xo14rY89RnH7iJYPacmIReDsZog+sh2ubZgBUFWwPRw2q8nmNP5iG8I9f1zi+t0EXVZn9Ki/MwbV9C3Tes7ZawzuE2LJ5S67jwNF0nbm9WMzh77XRcHPV3iNqCdV5/6YJu4TUIWk7DuHM8FdQdCtO43jhjbs49cRkZOw9ZtTr3f9hfZUrlSowhRJCSRlix72tLqnPDKgvA3BgCs29nZggGJa4AABjyDt/BddnLTasfRXKc/Nxd8lPONR8EPb4dsTR9iNx/4f1Rp0oTEhtTXkhFI6OYp31J6dOCLOqxKW6KHkhpI5ggoCrb8xTTZx9vEOVMUBQTYw1ZmdrwaWbVe6KrRGGUomim/eQc/wcAMCxUVCVGzgyhQLundtoHON4HmJXZ8MDVQpI/Hkzyg2o2aJPSWIqjrYfiRsffoWim3Eoz85D/qUbuPrmpzjR8zmU5xXU6vyEGEuAnz1WLmmD9q3dNY77eMkw+82mGDMyyEKRGQclL4TUEdnHzqIkPllntVswhuK78cg9ecFo1xQ5OVT/SRyHgiu3AAC8VIqQV8dqr3sCACIR7EMawHtA5YmtQROeqtZqIqG0DAWXblQ/3kdcGP+2an7Oo3sdMQYwVSJ39a0vanV+QowpKMABX81rib9WdcI3n7fET1+1wV+rOuGJfn6WDq3WKHkxg5JSJZJSS5BfYLszu4n1K0lIMaxdomHtDOH/zGDdiYcujIG3t1N/2/iDafDq3Vn1Df/IpGIRD4mLE9pvXq41SQmb+SLEbs7VSmAMqiasQ8HV28g+elZ3dVylEsm/bYU8M7vG1yDEFPx87NC+lTsiw13A87ZbVfdRlLyYUEZWGeZ/ewNDxh7H6CmnMWTsCcz8+CKu3MizdGikDtJVi6VSOx9Po10zeMpoiJ0dDd7YEVBNwPUZ1EP9vUgmRYetP6LlT1/CtW1ziF2dYRfkj0bvTEGP2K1waal9RY99oB+6HN4AZx2PP07s5gKX1pEGx/k4jV2zdWAKBfLOX63xNQghhqE6LyaSml6Kl9+JRV6eXGO297lLuTh/+SIWftwcndsZ9mZDiCE8e3eG1MdT70RWmb8PPLq1N9o17fy80XnPzzgz4mWUpWaCE4tVhe50bRnA8wh6cRRkvl6ahyUSBE18GkETn67W9Z2aNkT305uRd+4KLrz4Pgpv3NN+bY5D2Bvja7VcuupNFSva1a4wHiGkatTzYiLfr75XKXEBVH9XBYHh869vQKEwZKUFIYbhJRJEzn9Xb5vIRe8b/c3VtW0U+tw9iDa/LkXQi88geMpo+AztDeDhG7lIpF6R5P/0QER9/aFRrw8Aru2aI/rAr3Bp+XBfoIqhrIf/dWkdCf9Rg2t1Dc9enbRvofAI3t4Orh1a1uo6hJCqUZ0XE8jJk2PE+BidHz4rfPlBFHpEe+lvREg1JazdhOvvLUR5zn/DkxJPNzRbPBuB40aaLY6iOw+QuG4zSuJTIPX2QIPnhsG1bZRJrynI5Uj9ex/u//Ar8s5chlAmf5jAqFZb+Q7vh1ZrFkJSw40izz7zOtK3HdC+izbPIWz6RDRbPKt2N0FIPUVF6kycvCSnliAhuQT2diI0a+pSaVvxKzfyMfVd/ePjIhGHl54Ptdl9JYh1U5bJkbH7CMpSMmAX4APvgd3BS6WWDsssiuMScLT9SCiKSoDHkwyRCB5d2qLzvnXgqjvRGKoaL6cGv4i8s5dV83yUAiASAUolvAf3RLs/l0Ekqx//zqRuYIzh0rU87D+agcIiBRr42eOJ/n7w87Gr+slGVp33b5rzUg0JycVY+sNtnLmQqz7m4S7Bi8+FYsQgf3X5dQf7qrvlBYHBwYHGxolpiGRS+A3vZ+kwLOLe12ug1Ja4AIBSieyjZ5B5IAbe/bpW+9wSNxd0ObIBqVv2IvGXLShLzYB9SAMEvTgKPoN61CghIsRSiosV+ODLqzh7MRciEaeuAbV24wO8PC7Mqj9cU/JioOTUErzyTiyKijQrfWbnlGPJ8tsoKFSoX+iwYAcEBtgjMblE5/k4AD0605ARIcaW9Ovf2od1HuLEIiT/trVGyQugmlsU8OwQBDw7pKYhEmIVPvv6Bs5fzgWASvsgrVwXBy9PKQb3sc6aMPQxwUCr1t9HUZFS5z4R//frfWTnyAEAHMdh8thQnefiOOCJ/n7w9qzdRnGEEE2MMSgKivS3USghz8k1T0CEWKn7CUU4ejJL79zMtb8/MGpFbmOi5MUAxSVK7D+aAaWg+0VkjGH3oTT19/17+mDmK40hEXPgONUmWBU9ygN7++KtqU1MHTYh9Q7HcbBr4Ku/jVgEh1Dr2UmXEEs4fjqryvqSSSmlSEjSPYJgSTRsZIDcPDkUerYWBwBexCEto0zj2NNDG6BfDx/sOZSG5LRSuDiJ0a+nD4ICalBSnRBikOCXx+DWvP/prDXDFEoEvTjKzFERYl3K5MLDeZr639vK5NZZ0oOSFwM4O0nAcbq3jAFUE3C17dDp6iLBqOH0KY8Qcwl9fTySN+5A0c17Wue+hE2fAJcWTY16TWVpGVL+2omUv3aiPK8QzpGNEDxlDFzbNDPqdQgxlkahTpXmuTxOKuEQ4Gf+VUeGoGEjAzg7idGlg6feCuiCAAzo6WO+oAghWklcnNDl0Ho0GP8kOOl/Hyikvp6IXDwbkUtmG/V6JYmpONJmGC5Oeh/pO48g59hZJKz5C8c6PonrsxZZ7ZwBUr917eABDzcJdG11xPPAoD5+cHSwzj4OqvNioNv3CvHKO7FQKIVKvdEcB4wc7I+3Xw032vUIIbVXnpOHwhv3wMukcG7ZFHwtNmbUhjGGYx2eRMHVWzo3bGyx8nME0zAVsUKxl3Px9pxLUCqZxmIUngcC/e2xYnEbuDhXHlEwleq8f1PPi4GaNHTCN5+3hK+3ZheaWMxhzMhAzHiZJuASYm0k7q5wj24D17ZRRk9cACD7yGnkX7yuM3EBx+Hu4p+o94VYpTYt3PDT0rbo3c0boofFVl2cxXj+6SCsXNLWrIlLdVHPSzUJAsOFK7l4kFgCB3sRott7WPULTAgxnRsfLcW9r/5Pd/LyUJ+7B2EfHGCmqAipPoWSQV6mhJ2dCLyusSQTowq7JsTzHNq2dEfblu6WDoUQYmFMoahys0ZVO/3JDSGWJhZxEFvp/BZtaNiIEEJqyK1DS7Byhd42Ui932AVZZ5VSQmwVJS+EEFJDvsP7QubnBZ3VvngOIdNeAC+hoWVCjImSF0IIqSFeIkG7v5ZD5GAHTvTIRqsP5wx49emCxu+/bKHoCKm7KHkhhJBacO/UCj3O/4vQ116A1McTIkd7uLSMQIsVn6PDvyvBS6WWDpGQOodWGxFCCCHE4qjOCyGEEELqLEpeCCGEEGJTKHkhhBBCiE2xnYo0hBBCCDG6+MRi3I4rhFTCo00LNzg5Wn9qYP0REkIIIcToklJLMP/bm7hwJU99TCrl8czQALw8viHEIstsE2AISl4IIYSQeiYzuwyvvhuLvPxyjeNyuYANWxKRlSPHx29FWii6qtGcF0IIIaSe2bA5Abn55VAKlR9jDNh9MB037hSYPzADUfJCCCGE1COMMWzbmwpBS+JSQSTisGt/mvmCqiZKXgghhJB6pFzBUFSsf6dzQWDIzCkzU0TVZ9LkJScnB+PGjYOrqytcXV0xbtw45Obm6n3OxIkTwXGcxlfnzp1NGSYhhBBSb0jEHBwcRHrb8DwHL3eZmSKqPpMmL2PHjsWFCxewa9cu7Nq1CxcuXMC4ceOqfN6gQYOQkpKi/tqxY4cpwySEEELqDY7jMKy/H0R6MgClkmFwX1/zBVVNJlttdP36dezatQsnT55Ep06dAAA//fQToqOjcfPmTTRt2lTnc2UyGfz8/EwVGiGEEFKvPfdkEPYcSkd+gQJKQXOLQ44D+vXwQdPGzhaKrmom63mJiYmBq6urOnEBgM6dO8PV1RUnTpzQ+9xDhw7Bx8cH4eHhmDJlCtLT03W2LSsrQ35+vsYXIYQQQnTz8pRhxZI2aNFMcwNEqZTHmJGB+HCG7g4Ga2CynpfU1FT4+PhUOu7j44PU1FSdzxs8eDBGjRqFkJAQxMXF4eOPP0afPn1w7tw5yGSVx9/mz5+PefPmGTV2QgghpK5r4GePZfNb435CEe7EFUEq4dG2pW1U2K12z8vcuXMrTah9/Ovs2bMAVONqj2OMaT1eYfTo0XjiiSfQvHlzDBs2DDt37sStW7ewfft2re1nz56NvLw89VdCQkJ1b4kQQgipt0KDHNGvhw96RHvZROIC1KDn5fXXX8eYMWP0tgkNDcWlS5eQllZ5jXhGRgZ8fQ2fBOTv74+QkBDcvn1b6+MymUxrj4w1UCoZDsdkYsuOJNyPL4aDgwj9evjgySEB8PKwzpgJIYQQa1ft5MXLywteXl5VtouOjkZeXh5Onz6Njh07AgBOnTqFvLw8dOnSxeDrZWVlISEhAf7+/tUN1aIUSoY5C6/hcEwmeB4QBCAnrxy//BmPTduS8b8vW6FJQydLh0kIIYTYHJNN2I2MjMSgQYMwZcoUnDx5EidPnsSUKVMwdOhQjZVGERER2LJlCwCgsLAQ77zzDmJiYnD//n0cOnQIw4YNg5eXF5588klThWoSG/9OwJGTmQCgUcVQEIDiEgXe++wKFEqm49mEEEII0cWkdV7Wr1+PFi1aYMCAARgwYABatmyJX375RaPNzZs3kZen2tFSJBLh8uXLGDFiBMLDwzFhwgSEh4cjJiYGzs7Wu2TrcUolw5//JoHpyE0EAcjILMOJ01nmDYwQQgipA0w6M8fDwwO//vqr3jbskXd4e3t77N6925QhmUVGVhkys+V624hFHC5dy0OP6KqH4AghhBDyH9rbyAT0LKZSYwB4+tcnhBBCqo3ePk3Ax0uGAF876MthlEqGdq3czRYTIYQQUldQ8mICHMfhuaeCoGs6rogHghvYo0NrSl4IIYSQ6qLkxURGDvbHyMGq5d0ikaoPhuMADoCHuxSLPmkBnjdgfIkQQgghGmyjlJ4N4jgOb7/aBP16+OCfXSm4e78ITo4i9O3hg0G9feHoQP/0hBBCSE3QO6gJcRyH1s3d0Lq5m6VDIYQQQuoMSl4s4NK1PGz8JxGnY3PABIbmES54dkQgunTwtHRohBBCiNWj5MXM/tmVjMXf34aIB5QPK++ev5yLsxdzMW5UMF4ZH2bZAAkhhBArR8mLGcUnFmPJctUGk8rHtgwAgF/+jEeb5q7o2NbDAtERQgipiZxcOTZvT8bOA6nIL1DA39cOIwcHYEg/P8iktC7GFOhf1Yy27EyGvgVGPA/8tTXJfAERQgiplfikYox//Sx+/uMBUtPLUFyixL0HRVj6w228MfsCikuUlg6xTqLkxYyu3MjX6HF5nCAAV2/mmy8gQgghNcYYw0fzryG/oFxjA17GVFXUb9wpwIqf71ksvrqMho3MSCyquq6LSEy1XwghxBZcvp6Pew+KdD4uCMC2vamYOj4MDiYoj5GYXILt+1KRml4KV2cJ+vfyQbNwZ3CG7FFj4yh5MaMuHTxx5Ua+zt2mRTzQlVYcEUKITbh6Mx88D41el8fJ5QLuxReheYSr0a7LGMPKdXH49a8E9R55HMfhr21J6NrRE5++FwmZTGS061kjGjYyo6ED/GBvJ9I67+VhDV48M6yBmaMihBBSEyKe0/lh9PF2xrRpWzJ+/SsBgCpxEgTVfnkAEHMmC4sfLgypyyh5MSN3VymWzG0Be3uRxs7TPK8aLpr3XiQahTpZLkBCCCEGa9/avcrkxdlJjEZhxvu7rlAyrPvjgc7HBQbsPpiG9Mwyo13TGtGwkZm1bOaKP/+vE3bsT8Xp2BwolQwtm7li+EB/eHvKLB1eJUolQ+yVXGRly+HpIUWb5m7qvZoIIaQ+axjiiA6t3XHuUo7WoSOOA0aPCIRUYrx+glt3C5CdW15luxNnsjBycIDRrmttKHmxABdnCcaMDMKYkUGWDkWvA8cy8N1Pd5CZLVcf8/KQYvqUxujTzduCkRFCiHWY804kZn5yEbfvFannv4hEgFIJ9O/pg3Gjgo16vbIyPRNsHuKgmmtTl1HyQrQ6eDwDnyy8Vul4Zrb84fFmlMAQQuo9N1cJflzSFkdOZmLPwTTk5Jcj0N8eQwf4o01zV6Ov/AkNcqhykrDAgEahjka9rrWh5IVUolQyfPfTHb1t/vd/d9Az2ouGkAgh9Z5EwqNvdx/07e5j8mu5u0nRq6s3Dh/P0Fo3jOcBfx87tGnhZvJYLIkm7JJKLl7NRUaWXG+bjCw5Ll7LM1NEhBBCKrw5pTG8vWTqZdIVRCJAKuUx991I8EZe4WRtKHkhlWTlVD0ZDACysvUnOIQQQozP012K/1vaDmNGBsLJUVXPRSLmMKCXL1Z/3Q6R4S4WjtD0aNiIVOLlITVqO0IIIcbl5irBtEmNMHVCQ5SWCZBJ+Xo1jE89L6SSVlGu8PHSv2zb11uGVlHGqxhJCCGk+nieg4O9qF4lLgAlL0QLnucw85XGetu8OaVxnR9TJYQQYp0oeSFade/shfkfRsHPR7MHxs9HhvkfRqFHtJeFIiOEEFLf0ZwXolP3zl7o2tETl6/nITNbDi8PKVpEulKPCyGEEIui5IXoxfMcWkW5WToMQgghRI2GjQghhBBiUyh5IYQQQohNoWEjYrUKChXYeSAV5y/lQhBUu28/0d8P7q5UX4YQQuozSl6IVbp0LQ/vzruM4hIlGFMdizmbjTUbHuDz2c0Q3d7TsgESQmyOUskQczYL2/emIi2jDF4eUgzu54funb0grmd1Umwdx1jFW0PdkJ+fD1dXV+Tl5cHFpe6XSK6LsnLkeO6V0ygtVUJ47KeT4wCxiMPPy9ojuIGDZQIkhNicsjIlZn1+FWcu5Kh3Za74b/MIF3w1rwUcHejzvCVV5/2b5rwQq7N1d4rWxAUAGAMEgWHztmTzB0YIsVnLVt/FuYs5AFQJy6P/vXYrH4u/v2WhyEhNUPJCrM7x01laE5cKSgE4eirTfAERQmqEMYbL1/Ow51AaYs5mQV4uWCSO/IJybN2TqvPviiAAB45mICOrzLyBkRqjPjJidQz5A1eusMwfQUKIYc5fzsWiZbeQmFyiPubsJMaUF0Lx1BMNzBrLlRv5UCj0z5AQGBB7ORcDevmaKSpSG9TzQqxOVFMXiPRU8RXxQFQ92PKdEFt1+XoeZn58CUkpJRrHCwoVWLriDv74J9Gs8Qj6unIfoTSwHbE8Sl6I1XnyiQC9f0SUAvD0MPN+ciOEGG7Z6ntgjEHXcpCV6+JQVKwwWzwRTZzBG/Bu1zyCPhTZCkpeiNVpEuaENyY3AqDqZalQ8cdn3KggtG/lboHICCFVSUotwdUb+erJsNqUyQUciTHfvDUvDxl6dfXWmcCIeKBjG3cEBdAKRltBc16IVRo9MhCNwhzx+5ZEnLuUA8ZUn4rGjAxEt06W39H69r1CxCcVw8FehLYt3SGT0ucAQgAgO0deZRuRiENmdtXtjOmdV5vgfnwx4uKLNHqEOA7w87HDBzOamjUeUjuUvBCr1b6Vu9X0sBQXK8AAJCQXY9H/buPWvUL1Y06OIkwcE4LRIwLBcVToitRvnu5VV8BWKhm8PM1bKdvFWYKVS9pgx75U/Ls7BemZZfB0l2LoAD8MG+BPNV5sDL1ahOjAGMPew+nYsCUBt+8VAVB9SntcYZESy1bdQ3GJEi8+F2reIAmxMgF+9mgR6YKrN3UPHdnJePTsbP4eVHs7EZ4e2gBPD6U5c7aO+roJ0WHFz3H49KsbuBNXpD7GGHROQvz593iDuswJqetee7EReJ6DrkWDUyc0hAP1dJBaoOSFEC2u3MjD+k0JAHQnK48TGMO+I+kmjIoQ29A8wgXfft4KwYGaE2DdXCV497UmeIZWC5JaotSXEC22bE+GSAQolYY/RzUJkSp0EgIAraJc8cv37XHjdgGS00rh4iRGmxZuEIvpMzOpPUpeCNHi1r3CaiUugGoSoqe7zDQBEWKDOI5DZLgLIqmoJDEySoEJ0cLeTlTt53Ac0K+njwmiIYQQ8ihKXgjRoldXb60ri/QZNyrYoGWihBBCaoeSF0K0eKKfH5ydxAaVFHewF+HViWF46flQk8dFCCGE5rwQopWriwTffdEKb8+9jKxsOUQiDgCDUgk4O4nw+ouNIJHwcHQQo30rN8hk1R9mIoQQUjOUvBCiQ+MwJ/z1f51w6EQmzj/coqBlM1f07e5NyQohhFgQJS+E6CGR8Ojf0wf9aSIuIYRYDZrzQgghhBCbQj0vhBBC1AqLFNi5PxWnzudAoRQQ1dQFwwf6w9fbztKhEaJGyQshhBAAwPVb+XhrzmUUFinU22Kcv5SLX/9KwIczmmJAL1/LBkjIQzRsRAghBAWFCrw15zKKihUa+3kJgqp69OdLb+DG7QLLBUjIIyh5IYQQgp0HUlFYpIAgaH+c44E//k00b1CE6EDJCyGEEJw8m613B3WlEjh+Ost8ARGiByUvhBBCoFDo6HJ5hFKpJ7shxIxowi4hdUxpqRLHz2QhM0sOD3cpunXyrNFGk6R+iYpwwYWreTqHjXgeaEa7QxMrQckLIXXIv7tTsGzVXRSXKMHzqsmW9nYiTJ0YhqefaGDp8IgVGz4wAOs3Jeh8XBCAZ4bRzxCxDjRsREgdsXN/KhYtu4XiEiUAqD9Bl5Qq8fWKO/hnV7IFoyPWzt/XDrOnNwXH4eFeXioVm5OOGtYA3Tt7Wig6QjRRzwshdYBCyfDD2nt62/y4Lg5D+vpBIqHPLES7wX39EBzogI3/JCLmbDYEJUNkuDNGDQ9Ej86e4Diu6pMQYgaUvBBSB1y8kovs3HK9bfIKFDh3KRed23mYKSpii6KauuDT95pZOgxC9KLkhZA6IDdff+KibpdnWDtiPGcu5ODPfxNx+Xo+eB7o3M4Dzw4PRNPGzpYOjRCbRckLIXWAv69h+874+8pMHAl51Kr197Hm9wfqydMAsO9IBvYeSseHMyMwsDeV2yekJih5IaQOiGzijJBAe8QnlWgtNMZxqgSnZTNX8wdnhUpLlTh7MQcFhQoE+NmjZTMXo8/nOBObjTW/PwAAjeXHFbVSvvjmBlpEuiDAz96o19Xn9r1CbN6ehKs3CyCVcOjWyQvDBvrD011qthgIMQZKXgipAziOw9vTwjHz40sQBKaRwHCc6uvdaU3q/YRLxhh+/zsRazY8UK/KAoDAAHvMeiMcrZu7Ge1af/ybBBEPKPXUfvt7VwqmTWxotGvqs2FLAr5ffQ8iEadOoG7dK8T6TQn4al4LSmyJTTHpsoMvvvgCXbp0gYODA9zc3Ax6DmMMc+fORUBAAOzt7dGrVy9cvXrVlGESUie0beGG775oWWkuRZMwJ3zzWUt0aEMTdX/+Ix7fr76nkbgAQFJKCWZ8fAlXbuQb7VpXrufrTVwEAbh0Nc9o19PnTGw2vl+tWo32aJVcQQDKypR4d55qJ2lCbIVJkxe5XI5Ro0bh1VdfNfg5ixYtwtKlS7Fs2TKcOXMGfn5+6N+/PwoKaDdTQqrSKsoN/7e0LX77oQO++6Ilfl3eHqu/bYe2Ld0tHZrF5eWXY+2GB1ofYwwQBIaV6/QvN68O3oC/rmKReXrCNmxJhEhHPAIDikuU2Lk/1SyxEGIMJk1e5s2bh5kzZ6JFixYGtWeM4ZtvvsGHH36Ip556Cs2bN8fPP/+M4uJi/Pbbb6YMlZA6JTjQAW1buiM0yNHSoViNg8czoBR0780jCEDs5TykZ5YZ5Xqd23loFHt7HMcBncy0bP385Vy9vUAAcO5SrlliIcQYrKpaVVxcHFJTUzFgwAD1MZlMhp49e+LEiRNan1NWVob8/HyNL0IIeVxWjhw8X3VPR3au3CjXGzUiEExHssRxgEzGY2h/P6Ncqyr6dotWP057LhIbYlXJS2qqqtvS11dz+aCvr6/6scfNnz8frq6u6q+goCCTx0kIsT3enjIIBuyK7OVhnJU3EY2d8cGMCPC85hBSReKy+JMWcHczzyqfFhEueoexOA5oGUUTdontqHbyMnfuXHAcp/fr7NmztQrq8RURjDGdqyRmz56NvLw89VdCgu6NxQgh9Vfvrt6QSHT3vPA80KGNO7w8jFcLZ1AfX2xY2RFjngxC8wgXtI5yxZQXQvHnT53QpoWb0a5TldEjA3XuFs1xgFTCY0hf8/QCEWIM1V4q/frrr2PMmDF624SGhtYoGD8/1S9Pamoq/P391cfT09Mr9cZUkMlkkMmo8BYhRD9nJzFeHh+GZasqT8rleUAs5vHqhDCjX7eBn73ZlkPr0r2zF8aNCsIvfyZAxHPquT8iHuBFHL74IApurhKLxkhIdVQ7efHy8oKXl5cpYkFYWBj8/Pywd+9etGnTBoBqxdLhw4excOFCk1yTEFJ/jBkZBDuZCP+3/r7GVgmNQ53w7mtNEN6o7pbsf2V8Q7Rr5Y7N21RF6iQSDt07e+GpJwIQFOBg6fAIqRaTFqmLj49HdnY24uPjoVQqceHCBQBA48aN4eTkBACIiIjA/Pnz8eSTT4LjOMyYMQNffvklmjRpgiZNmuDLL7+Eg4MDxo4da8pQCSH1xMjBARja3w8Xr+ahsEiBAH97NAlzsnRYZtG+lTvat6Jl88T2mTR5+eSTT/Dzzz+rv6/oTTl48CB69eoFALh58yby8v4r1PTee++hpKQE06ZNQ05ODjp16oQ9e/bA2bnufiIihJiXWMyjHb2JE2KzOMaqWkRnW/Lz8+Hq6oq8vDy4uLhYOhxCCCGEGKA6799WtVSaEGIaJaVKZOfIoTBgqTAhhFg72piRkDrsyo18rN34AKfOZoMBcHQQYfhAf4x7NhguTrS6hBBim6jnhZA66vjpLLz2/gWcPp+tLp5aVKzExn8SMfWdWOQXlOt9PiGEWCtKXgipg8rKlPhs6XUIjFUqTiYIQGJKCX769b5FYiOEkNqi5IWQOujg8UwUFil17mkjCMCOfakoLVWaNzBCCDECmvNCSB0UF18EsYjTO0G3TC4gPbMMwYG1K1DGGMO1WwWITyqGg70YHVq7w8FeVKtzEkKIPpS8EFIH2duJIBhQBUEmq13n67Vb+Zj/7U3ExRerj9nJeLwwKhjjRwUbtIszIYRUFw0bEVIH9Yj20rkRH6DajK9JQ0f4etvV+Bp34grxxuyLeJBYrHG8tEzA//16HyvXxdX43IQQog8lL4TUQQ1DHNG1oyd4Hb/hjAGTxoTW6hr/9+t9lCsEnUnShs0JyMgqq9U1CCFEG0peCKmj5rwdgQ6tVSXwRSIOIhEHjlP9/1tTG6NHdM03WM0vLMfxM1l6e3fAAXsPp9f4GoQQogvNeSGkjnJwEOOreS1x7VY+DhzNQFGJEkEB9hjcxxfubtJanTsvv1znSqYKPMchK0deq+sQQog2lLwQUsc1C3dBs3Dj7vPl5iIFzwGCngRGKTB4e9YuSSKEEG1o2IgQUm3OTmJ0j/bSOacGUPW89O/pa76gCCH1BiUvhJAaefmFMNjJRDoTmAljguHpTj0vhBDjo+SFEFIjIUEOWL6wNSIaO2scd3YSY/pLjTBpTIiFIiOE1HU054UQUmONw5zw41dtce9BERKSiuFgL0Kr5m6QSuhzESHEdCh5IYTUWsMQRzQMcbR0GISQeoI+HhFCCCHEplDyQgghhBCbQskLIYQQQmwKJS+EEEIIsSmUvBBCCCHEplDyQgghhBCbQskLIYQQQmwKJS+EEEIIsSmUvBBCCCHEptS5CruMMQBAfn6+hSMhhBBCiKEq3rcr3sf1qXPJS0FBAQAgKCjIwpEQQgghpLoKCgrg6uqqtw3HDElxbIggCEhOToazszM4jjPLNfPz8xEUFISEhAS4uLiY5ZrmVJfvj+7NdtXl+6vL9wbU7fury/cGmPb+GGMoKChAQEAAeF7/rJY61/PC8zwCAwMtcm0XF5c6+cNaoS7fH92b7arL91eX7w2o2/dXl+8NMN39VdXjUoEm7BJCCCHEplDyQgghhBCbQsmLEchkMsyZMwcymczSoZhEXb4/ujfbVZfvry7fG1C3768u3xtgPfdX5ybsEkIIIaRuo54XQgghhNgUSl4IIYQQYlMoeSGEEEKITaHkhRBCCCE2hZIXQgghhNgUSl4M8MUXX6BLly5wcHCAm5ubQc9hjGHu3LkICAiAvb09evXqhatXr2q0KSsrwxtvvAEvLy84Ojpi+PDhSExMNMEd6JeTk4Nx48bB1dUVrq6uGDduHHJzc/U+h+M4rV+LFy9Wt+nVq1elx8eMGWPiu9FUk3ubOHFipbg7d+6s0cZWX7vy8nK8//77aNGiBRwdHREQEIDx48cjOTlZo50lXrvly5cjLCwMdnZ2aNeuHY4ePaq3/eHDh9GuXTvY2dmhYcOGWLFiRaU2mzZtQrNmzSCTydCsWTNs2bLFVOFXqTr3t3nzZvTv3x/e3t5wcXFBdHQ0du/erdFm7dq1Wn8HS0tLTX0rlVTn3g4dOqQ17hs3bmi0s9XXTtvfD47jEBUVpW5jLa/dkSNHMGzYMAQEBIDjOPz9999VPsdqfu8YqdInn3zCli5dyt566y3m6upq0HMWLFjAnJ2d2aZNm9jly5fZ6NGjmb+/P8vPz1e3mTp1KmvQoAHbu3cvO3/+POvduzdr1aoVUygUJroT7QYNGsSaN2/OTpw4wU6cOMGaN2/Ohg4dqvc5KSkpGl+rV69mHMexu3fvqtv07NmTTZkyRaNdbm6uqW9HQ03ubcKECWzQoEEacWdlZWm0sdXXLjc3l/Xr149t3LiR3bhxg8XExLBOnTqxdu3aabQz92v3+++/M4lEwn766Sd27do19uabbzJHR0f24MEDre3v3bvHHBwc2JtvvsmuXbvGfvrpJyaRSNhff/2lbnPixAkmEonYl19+ya5fv86+/PJLJhaL2cmTJ012H7pU9/7efPNNtnDhQnb69Gl269YtNnv2bCaRSNj58+fVbdasWcNcXFwq/S6aW3Xv7eDBgwwAu3nzpkbcj/7u2PJrl5ubq3FfCQkJzMPDg82ZM0fdxlpeux07drAPP/yQbdq0iQFgW7Zs0dvemn7vKHmphjVr1hiUvAiCwPz8/NiCBQvUx0pLS5mrqytbsWIFY0z1Ay6RSNjvv/+ubpOUlMR4nme7du0yeuy6XLt2jQHQ+MGKiYlhANiNGzcMPs+IESNYnz59NI717NmTvfnmm8YKtdpqem8TJkxgI0aM0Pl4XXvtTp8+zQBo/DE292vXsWNHNnXqVI1jERERbNasWVrbv/feeywiIkLj2CuvvMI6d+6s/v7ZZ59lgwYN0mgzcOBANmbMGCNFbbjq3p82zZo1Y/PmzVN/b+jfI1Or7r1VJC85OTk6z1mXXrstW7YwjuPY/fv31ces5bV7lCHJizX93tGwkQnExcUhNTUVAwYMUB+TyWTo2bMnTpw4AQA4d+4cysvLNdoEBASgefPm6jbmEBMTA1dXV3Tq1El9rHPnznB1dTU4jrS0NGzfvh2TJ0+u9Nj69evh5eWFqKgovPPOOygoKDBa7FWpzb0dOnQIPj4+CA8Px5QpU5Cenq5+rC69dgCQl5cHjuMqDYma67WTy+U4d+6cxr8nAAwYMEDnfcTExFRqP3DgQJw9exbl5eV625jzNQJqdn+PEwQBBQUF8PDw0DheWFiIkJAQBAYGYujQoYiNjTVa3Iaozb21adMG/v7+6Nu3Lw4ePKjxWF167VatWoV+/fohJCRE47ilX7uasKbfuzq3q7Q1SE1NBQD4+vpqHPf19cWDBw/UbaRSKdzd3Su1qXi+OaSmpsLHx6fScR8fH4Pj+Pnnn+Hs7IynnnpK4/jzzz+PsLAw+Pn54cqVK5g9ezYuXryIvXv3GiX2qtT03gYPHoxRo0YhJCQEcXFx+Pjjj9GnTx+cO3cOMpmsTr12paWlmDVrFsaOHauxQ6w5X7vMzEwolUqtvy+67iM1NVVre4VCgczMTPj7++tsY87XCKjZ/T3uq6++QlFREZ599ln1sYiICKxduxYtWrRAfn4+vv32W3Tt2hUXL15EkyZNjHoPutTk3vz9/fHjjz+iXbt2KCsrwy+//IK+ffvi0KFD6NGjBwDdr6+tvXYpKSnYuXMnfvvtN43j1vDa1YQ1/d7V2+Rl7ty5mDdvnt42Z86cQfv27Wt8DY7jNL5njFU69jhD2hjC0PsDKsdZ3ThWr16N559/HnZ2dhrHp0yZov7/5s2bo0mTJmjfvj3Onz+Ptm3bGnRubUx9b6NHj1b/f/PmzdG+fXuEhIRg+/btlRK06pzXUOZ67crLyzFmzBgIgoDly5drPGaq106f6v6+aGv/+PGa/A6aSk1j2bBhA+bOnYt//vlHI1nt3LmzxkTyrl27om3btvjf//6H7777zniBG6A699a0aVM0bdpU/X10dDQSEhKwZMkSdfJS3XOaWk1jWbt2Ldzc3DBy5EiN49b02lWXtfze1dvk5fXXX69y9URoaGiNzu3n5wdAlaX6+/urj6enp6szUj8/P8jlcuTk5Gh8gk9PT0eXLl1qdN1HGXp/ly5dQlpaWqXHMjIyKmXP2hw9ehQ3b97Exo0bq2zbtm1bSCQS3L59u1ZvgOa6twr+/v4ICQnB7du3AdSN1668vBzPPvss4uLicODAAY1eF22M9dpp4+XlBZFIVOmT2aO/L4/z8/PT2l4sFsPT01Nvm+q89sZQk/ursHHjRkyePBl//vkn+vXrp7ctz/Po0KGD+ufUHGpzb4/q3Lkzfv31V/X3deG1Y4xh9erVGDduHKRSqd62lnjtasKqfu+MOoOmjqvuhN2FCxeqj5WVlWmdsLtx40Z1m+TkZItN+jx16pT62MmTJw2e9DlhwoRKK1V0uXz5MgPADh8+XON4q6O291YhMzOTyWQy9vPPPzPGbP+1k8vlbOTIkSwqKoqlp6cbdC1Tv3YdO3Zkr776qsaxyMhIvRN2IyMjNY5NnTq10sTBwYMHa7QZNGiQxSZ9Vuf+GGPst99+Y3Z2dlVOoqwgCAJr3749mzRpUm1Crbaa3Nvjnn76ada7d2/197b+2jH238Tky5cvV3kNS712j4KBE3at5feOkhcDPHjwgMXGxrJ58+YxJycnFhsby2JjY1lBQYG6TdOmTdnmzZvV3y9YsIC5urqyzZs3s8uXL7PnnntO61LpwMBAtm/fPnb+/HnWp08fiy23bdmyJYuJiWExMTGsRYsWlZbbPn5/jDGWl5fHHBwc2A8//FDpnHfu3GHz5s1jZ86cYXFxcWz79u0sIiKCtWnTxqz3V917KygoYG+//TY7ceIEi4uLYwcPHmTR0dGsQYMGdeK1Ky8vZ8OHD2eBgYHswoULGss0y8rKGGOWee0qlqOuWrWKXbt2jc2YMYM5OjqqV2jMmjWLjRs3Tt2+YsnmzJkz2bVr19iqVasqLdk8fvw4E4lEbMGCBez69etswYIFFl9ua+j9/fbbb0wsFrPvv/9e53L1uXPnsl27drG7d++y2NhYNmnSJCYWizWSWWu8t6+//ppt2bKF3bp1i125coXNmjWLAWCbNm1St7Hl167CCy+8wDp16qT1nNby2hUUFKjfzwCwpUuXstjYWPXKQ2v+vaPkxQATJkxgACp9HTx4UN0GAFuzZo36e0EQ2Jw5c5ifnx+TyWSsR48elTLwkpIS9vrrrzMPDw9mb2/Phg4dyuLj4810V//Jyspizz//PHN2dmbOzs7s+eefr7SM8fH7Y4yxlStXMnt7e631P+Lj41mPHj2Yh4cHk0qlrFGjRmz69OmV6qWYWnXvrbi4mA0YMIB5e3sziUTCgoOD2YQJEyq9Lrb62sXFxWn9WX7059lSr93333/PQkJCmFQqZW3bttXo5ZkwYQLr2bOnRvtDhw6xNm3aMKlUykJDQ7Um0X/++Sdr2rQpk0gkLCIiQuMN0tyqc389e/bU+hpNmDBB3WbGjBksODiYSaVS5u3tzQYMGMBOnDhhxjv6T3XubeHChaxRo0bMzs6Oubu7s27durHt27dXOqetvnaMqXpn7e3t2Y8//qj1fNby2lX0Dun6ObPm3zuOsYezbQghhBBCbADVeSGEEEKITaHkhRBCCCE2hZIXQgghhNgUSl4IIYQQYlMoeSGEEEKITaHkhRBCCCE2hZIXQgghhNgUSl4IIYQQYlMoeSGEEEKITaHkhRBCCCE2hZIXQgghhNiU/wcFcX6TENQo1QAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-17T06:02:45.883823Z",
     "start_time": "2025-04-17T06:02:45.874823Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn import datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import SVC\n",
    "import time\n",
    "\n",
    "# 加载鸢尾花数据集\n",
    "iris = datasets.load_iris()\n",
    "X = iris.data\n",
    "y = iris.target\n",
    "\n",
    "# 划分训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "\n",
    "# 特征标准化\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "# 初始化 SVM 模型\n",
    "model = SVC(kernel='rbf', C=1.0, gamma='scale')\n",
    "\n",
    "# 计时开始\n",
    "start_time = time.time()\n",
    "\n",
    "# 训练模型\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# 计时结束\n",
    "end_time = time.time()\n",
    "\n",
    "print(\"训练耗时：{:.4f} 秒\".format(end_time - start_time))\n",
    "\n",
    "# 模型测试\n",
    "accuracy = model.score(X_test, y_test)\n",
    "print(\"测试集准确率：{:.2f}%\".format(accuracy * 100))"
   ],
   "id": "5f904e1b0ca69717",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练耗时：0.0010 秒\n",
      "测试集准确率：100.00%\n"
     ]
    }
   ],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-17T06:02:45.963665Z",
     "start_time": "2025-04-17T06:02:45.958664Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 训练集准确率\n",
    "train_acc = model.score(X_train, y_train)\n",
    "# 测试集准确率\n",
    "test_acc = model.score(X_test, y_test)\n",
    "\n",
    "print(\"训练集准确率：{:.2f}%\".format(train_acc * 100))\n",
    "print(\"测试集准确率：{:.2f}%\".format(test_acc * 100))\n",
    "\n",
    "# 判断是否过拟合\n",
    "if train_acc - test_acc > 0.1:\n",
    "    print(\"⚠️ 可能存在过拟合现象！\")\n",
    "else:\n",
    "    print(\"✅ 模型没有明显过拟合。\")"
   ],
   "id": "1462aac4bfb92695",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集准确率：96.19%\n",
      "测试集准确率：100.00%\n",
      "✅ 模型没有明显过拟合。\n"
     ]
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-17T06:02:46.169160Z",
     "start_time": "2025-04-17T06:02:45.988188Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.model_selection import learning_curve\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "train_sizes, train_scores, test_scores = learning_curve(\n",
    "    model, X, y, cv=5, train_sizes=np.linspace(0.1, 1.0, 10)\n",
    ")\n",
    "\n",
    "train_mean = np.mean(train_scores, axis=1)\n",
    "test_mean = np.mean(test_scores, axis=1)\n",
    "\n",
    "plt.plot(train_sizes, train_mean, label=\"Train\")\n",
    "plt.plot(train_sizes, test_mean, label=\"Validation\")\n",
    "plt.xlabel(\"Training Size\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend()\n",
    "plt.title(\"Learning Curve\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ],
   "id": "86599457e0588c5",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\an\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:547: FitFailedWarning: \n",
      "15 fits failed out of a total of 50.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "15 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"D:\\an\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 895, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"D:\\an\\Lib\\site-packages\\sklearn\\base.py\", line 1474, in wrapper\n",
      "    return fit_method(estimator, *args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"D:\\an\\Lib\\site-packages\\sklearn\\svm\\_base.py\", line 199, in fit\n",
      "    y = self._validate_targets(y)\n",
      "        ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"D:\\an\\Lib\\site-packages\\sklearn\\svm\\_base.py\", line 743, in _validate_targets\n",
      "    raise ValueError(\n",
      "ValueError: The number of classes has to be greater than one; got 1 class\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-17T06:02:46.210087Z",
     "start_time": "2025-04-17T06:02:46.200064Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "scores = cross_val_score(model, X, y, cv=5)\n",
    "print(\"交叉验证准确率平均值：{:.2f}%\".format(scores.mean() * 100))"
   ],
   "id": "b1bf94b5f8d217b1",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "交叉验证准确率平均值：96.67%\n"
     ]
    }
   ],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-17T06:02:46.217573Z",
     "start_time": "2025-04-17T06:02:46.214574Z"
    }
   },
   "cell_type": "code",
   "source": "",
   "id": "a6be856f94c46a77",
   "outputs": [],
   "execution_count": null
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
